Phase Amplitude Coupling workflow for mapping EEG signals to standard brain networks

Human brain mapping or neuroimaging plays a pivotal role in understanding the intricacies of the human brain and paving the way for potential therapeutic interventions. Studying the standard brain networks, typically obtained from fMRI, provide valuable insights into the fundamental organization of...

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Detalles Bibliográficos
Autores principales: Fernández Corazza, Mariano, Hathaway, Evan, Morgan, Kyle, Shusterman, Roma, Andrinolo Olivares, Dante Camilo, Luu, Phan, Muravchik, Carlos Horacio, Tucker, Don
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2023
Materias:
Ingeniería
brain networks
phase amplitude coupling
confirmatory factor analysis
electroencephalography inverse problem
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/167166
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-167166
record_format dspace
spelling I19-R120-10915-1671662024-12-09T14:39:16Z http://sedici.unlp.edu.ar/handle/10915/167166 Phase Amplitude Coupling workflow for mapping EEG signals to standard brain networks Fernández Corazza, Mariano Hathaway, Evan Morgan, Kyle Shusterman, Roma Andrinolo Olivares, Dante Camilo Luu, Phan Muravchik, Carlos Horacio Tucker, Don 2023-11 2023 2024-06-12T17:28:59Z en Ingeniería brain networks phase amplitude coupling confirmatory factor analysis electroencephalography inverse problem Human brain mapping or neuroimaging plays a pivotal role in understanding the intricacies of the human brain and paving the way for potential therapeutic interventions. Studying the standard brain networks, typically obtained from fMRI, provide valuable insights into the fundamental organization of the human brain. In this work we present a workflow for processing electroencephalography (EEG) signals to determine the correlations of the phase-amplitude coupling (PAC) of the standard brain networks during a given timewindow. We validate this pipeline with synthetic signals on realistic head models of two subjects with the ultimate goal of studying the changes of these networks during different sleep stages. The proposed workflow consists of: mapping the signals to the source space, averaging per Brodmann Area (BA), low and high pass filtering, computing the modulation index per low-high frequency pair, generating surrogate data to obtain significance thresholds, obtaining the significant PAC signals, computing the signal and noise covariance matrices, removing the model bias, and applying confirmatory factor analysis (CEA) to determine the relevance of each standard brain network. We included the novelty of using CEA instead of principal component analysis as done in previous studies. We tested the workflow with synthetic signals, and it performed as expected. Next steps will be fine-tuning it and improving its robustness before processing real signals during sleep that we already have collected for the two subjects of the head models used here. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 525-530
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ingeniería
brain networks
phase amplitude coupling
confirmatory factor analysis
electroencephalography inverse problem
spellingShingle Ingeniería
brain networks
phase amplitude coupling
confirmatory factor analysis
electroencephalography inverse problem
Fernández Corazza, Mariano
Hathaway, Evan
Morgan, Kyle
Shusterman, Roma
Andrinolo Olivares, Dante Camilo
Luu, Phan
Muravchik, Carlos Horacio
Tucker, Don
Phase Amplitude Coupling workflow for mapping EEG signals to standard brain networks
topic_facet Ingeniería
brain networks
phase amplitude coupling
confirmatory factor analysis
electroencephalography inverse problem
description Human brain mapping or neuroimaging plays a pivotal role in understanding the intricacies of the human brain and paving the way for potential therapeutic interventions. Studying the standard brain networks, typically obtained from fMRI, provide valuable insights into the fundamental organization of the human brain. In this work we present a workflow for processing electroencephalography (EEG) signals to determine the correlations of the phase-amplitude coupling (PAC) of the standard brain networks during a given timewindow. We validate this pipeline with synthetic signals on realistic head models of two subjects with the ultimate goal of studying the changes of these networks during different sleep stages. The proposed workflow consists of: mapping the signals to the source space, averaging per Brodmann Area (BA), low and high pass filtering, computing the modulation index per low-high frequency pair, generating surrogate data to obtain significance thresholds, obtaining the significant PAC signals, computing the signal and noise covariance matrices, removing the model bias, and applying confirmatory factor analysis (CEA) to determine the relevance of each standard brain network. We included the novelty of using CEA instead of principal component analysis as done in previous studies. We tested the workflow with synthetic signals, and it performed as expected. Next steps will be fine-tuning it and improving its robustness before processing real signals during sleep that we already have collected for the two subjects of the head models used here.
format Objeto de conferencia
Objeto de conferencia
author Fernández Corazza, Mariano
Hathaway, Evan
Morgan, Kyle
Shusterman, Roma
Andrinolo Olivares, Dante Camilo
Luu, Phan
Muravchik, Carlos Horacio
Tucker, Don
author_facet Fernández Corazza, Mariano
Hathaway, Evan
Morgan, Kyle
Shusterman, Roma
Andrinolo Olivares, Dante Camilo
Luu, Phan
Muravchik, Carlos Horacio
Tucker, Don
author_sort Fernández Corazza, Mariano
title Phase Amplitude Coupling workflow for mapping EEG signals to standard brain networks
title_short Phase Amplitude Coupling workflow for mapping EEG signals to standard brain networks
title_full Phase Amplitude Coupling workflow for mapping EEG signals to standard brain networks
title_fullStr Phase Amplitude Coupling workflow for mapping EEG signals to standard brain networks
title_full_unstemmed Phase Amplitude Coupling workflow for mapping EEG signals to standard brain networks
title_sort phase amplitude coupling workflow for mapping eeg signals to standard brain networks
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/167166
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